MANUFACTURING PROCESSES TRANSFORMATION IN AERONAUTICAL SECTOR IN AN INDUSTRY 4.0 CONTEXT

10.6036/9938 ◽  
2021 ◽  
Vol 96 (3) ◽  
pp. 233-234
Author(s):  
LEANDRO RUIZ ◽  
MANUEL TORRES ◽  
ALEJANDRO GOMEZ VILANOVA ◽  
SEBASTIAN DIAZ DIAZ ◽  
FRANCISCO CAVAS MARTINEZ
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Industry 4.0 ◽  
Added Value ◽  
Manufacturing Processes ◽  
Production Chain ◽  
High Productivity ◽  
Robotic Cells ◽  
Major Transformation ◽  
The Creation

Adoption by the aeronautical sector of developments and technologies of the so-called Industry 4.0 is a major transformation, due to the added value that these new processes bring to the production chain. It is in this context, in which the relevance of the digitalization and automation of all manufacturing processes is observed, with the increasingly widespread implantation of robotic cells and other technologies such as systems of vision and artificial intelligence, will lead to a new digital scenario that will allow the creation in real time of reconfigurable and sustainable spaces with high productivity and reliability.

scholarly journals Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots

2019 ◽  
Vol 8 (5) ◽  
pp. 143 ◽  
Author(s):  
Rabab Benotsmane ◽  
György Kovács ◽  
László Dudás
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Optimal Operation ◽  
Added Value ◽  
Smart Factory ◽  
Motion Path ◽  
Robot Arm ◽  
Quantitative Research Methods ◽  
Smart Factories

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.

scholarly journals Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5031
Author(s):  
Javier Villalba-Diez ◽  
Miguel Gutierrez ◽  
Mercedes Grijalvo Martín ◽  
Tomas Sterkenburgh ◽  
Juan Carlos Losada ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Industry 4.0 ◽  
Numerical Control ◽  
Manufacturing Processes ◽  
Bayesian Decision ◽  
Cnc Machine ◽  
Digital Twin ◽  
Decision Networks ◽  
Computational Performance

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

scholarly journals Constructive activities of actors: strategy of intensification of added value of higher education

2021 ◽  
Vol 27 (3) ◽  
pp. 77-83
Author(s):  
V. M. Nesterenko ◽  
N. M. Melnik
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
New Product ◽  
Added Value ◽  
Professional Activity ◽  
Distributed Network ◽  
Area Of Interest ◽  
Conscious Choice ◽  
The Creation ◽  
The Impact

Digitalization of the economy is fundamentally changing the professional environment. Artificial intelligence is becoming a full-fledged participant in professional activity along with humans. The creation of a product with in-demand properties is ensured by the interaction of a changing environment, natural and artificial intelligence. It is important to train a specialist-actor who is able to create technical systems with artificial intelligence and build productive relationships with such systems in real time. The new reality has exacerbated the problem of the intensification of the increase in the value of higher education. The article argues that the added value of higher education is realized in the transition of a university graduate from a passive and reactive acquirer of knowledge to an active specialist-actor, creator of a qualitatively new product in almost any area of interest due to holistically presentation of productive activity and a conscious choice of the type of relationship with participants in professional activity including artificial intelligence. The necessity and possibility of transition to the strategy of organizing the structure and content of higher education based on the transfer of the ontological status of both subjects and the environment on the relations between them, providing the effect of interaction of heterogeneous participants in a distributed network of relations and mediating the interobjectivity (integrity of activity) of the impact on the object of all participants in the creation is proved.

Artificial Intelligence and Backshoring Strategies

2021 ◽  
pp. 227-255
Author(s):  
Steffen Kinkel ◽  
Mauro Capestro ◽  
Eleonora Di Maria
Keyword(s):  
Artificial Intelligence ◽  
Empirical Evidence ◽  
Industry 4.0 ◽  
Positive Impact ◽  
Manufacturing Processes ◽  
Manufacturing Companies ◽  
Production Processes ◽  
Strategic Factors ◽  
Using Data ◽  
The Relationship

The Industry 4.0 technologies, such as artificial intelligence (AI), are transforming the manufacturing processes and affecting the location of manufacturing activities across countries, with a potentially positive impact on the backshoring of production processes. The chapter aims at providing empirical evidence on the relationship between AI and relocation, exploring how AI is related to both the offshoring and backshoring strategies, using data from an international sample of 124 German and Italian manufacturing companies. Following the investigation of AI use by German and Italian manufacturing companies, the study analyses the differences in some strategic factors and the offshoring and backshoring decisions between German and Italian companies, AI users and non-users, and between the German and Italian AI users. Results show that the most important differences concern AI users and non-users and indicate a higher value of AI use for backshoring rather than offshoring strategies. The findings enable the derivation of both theoretical and managerial contributions.

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

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